Invited Speakers

Director of Knowledge Discovery Laboratory
College of Information and Computer Sciences
University of Massachusetts Amherst, USA

Inferring Causal Models of Complex Relational and Dynamic Systems

Over the past 25 years, surprisingly effective techniques have been developed
for inferring causal models from observational data. While traditional models
reason about a given system by assuming that its behavior is stationary,
causal models reason about how a system will behave under intervention.
Unfortunately, nearly all existing methods for causal inference assume that
data instances are independent and identically distributed, making them
inappropriate for analyzing many social, economic, biological, and
computational systems. In this talk, I will explain the key ideas,
representations, and algorithms for causal inference, and I will describe very
recent developments that extend those techniques to complicated systems with
relational and dynamic behavior. I will describe practical methods for
evaluating methods for causal inference and identify some of the most pressing
research questions and new technical frontiers.
David Jensen is Professor of Computer Science at the University of
Massachusetts Amherst. He serves as Director of the Knowledge Discovery
Laboratory and Associate Director of the Computational Social Science
Institute. He received his doctorate from Washington University in St. Louis
in 1992. From 1991 to 1995, he served as an analyst with the Office of
Technology Assessment, an agency of the United States Congress. His research
focuses on machine learning and causal inference in complex data, with
applications to social network analysis, computational social science, fraud
detection, and management of large technical systems. He has served on the
Executive Committee of the ACM Special Interest Group on Knowledge Discovery
and Data Mining and on the program committees of many leading conferences,
including the International Conference on Machine Learning, the International
Conference on Knowledge Discovery and Data Mining, and the Conference on
Uncertainty in Artificial Intelligence. He was a member of the 2006-2007
Defense Science Study Group, and served for six years on DARPA's Information
Science and Technology (ISAT) Group. In 2011, he won the Outstanding Teaching
Award from the UMass College of Natural Science.

Machine Learning and Logic — the beginnings of a new computer science?

Our long-term research goal in Cognitive Computing Research at IBM is to
develop systems that know deeply, learn continuously, reason with purpose
and interact naturally. To further this agenda, we are focusing on a few
deep domains. This talk will address the challenges of building cognitive
assistants in compliance — assistants that deal with understanding and
reasoning about the myriad (corporate, financial, privacy, ethical) laws
and regulations within the context of which modern international
businesses must operate. An interim goal for the compliance cognitive
assistant is to clear the Uniform CPA exam, a professional certification
attempted by master's level students. We will outline the tremendous
technical challenges underlying this goal and our current approaches. We
believe the key to achieving this goal is bringing together researchers
in natural language understanding, machine learning, and knowledge
representation/reasoning for a concerted attack on this problem.
Dr Vijay Saraswat joined IBM Research in 2003 after a year as a professor at
Penn State, a couple of years at start-ups, and 13 years at Xerox PARC
and AT&T Research. His main interests are in programming languages,
constraints, logic, concurrency, and, now, machine learning. In 2004, he
founded and co-led the X10 project, a modern object-oriented programming
language intended for scalable concurrent computing. In 2015, he was
asked to join the Cognitive Computing Research division at TJ Watson,
where he now guides long-term research in the New Computer Science — the
confluence of natural language understanding, (deep) machine learning,
and knowledge representation and reasoning.
Vijay has collaborated extensively with colleagues across logic, AI,
programming languages and systems; these collaborations have been
recognized with an ACM Doctoral Dissertation award, a
best-paper-in-20-years award from ALP, and a best-paper-in-10-years award
from ACM. Vijay has a B Tech degree from IIT Kanpur, and an MS and PhD
from Carnegie-Mellon University.

Probabilistic programming aims to enable the next generation of data scientists to easily and efficiently create the kinds of probabilistic models needed to inform decisions and accelerate scientific discovery in the realm of big data and big models.
Model creation and the learning of probabilistic models from data are key problems in data science. Probabilistic models are used for forecasting, filling in missing data, outlier detection, cleanup, classification, and scientific understanding of data in every academic field and every industrial sector. While much work in probabilistic modeling has been based on hand-built models and laboriously-derived inference methods, future advances in model-based data science will require the development of much more powerful automated tools than currently exist.
In the absence of such automated tools, probabilistic models have traditionally co-evolved with methods for performing inference. In both academic and industrial practice, specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. This intertwined nature of modeling and computation leaves much of the promise of probabilistic modeling out of reach for even expert data scientists. The emerging field of probabilistic programming will reduce the technical and cognitive overhead associated with writing and designing novel probabilistic models by both introducing a programming (modeling) language abstraction barrier and automating inference.
The automation of inference, in particular, will lead to massive productivity gains for data scientists, much akin to how high-level programming languages and advances in compiler technology have transformed software developer productivity. What is more, not only will traditional data science be accelerated, but the number and kind of people who can do data science also will be dramatically increased.
My talk will touch on all of this, explain how to develop such probabilistic programming languages, highlight some exciting ways such languages are starting to be used, and introduce what I think are some of the most important challenges facing the field as we go forward.
Dr. Wood is an associate professor in the Department of Engineering Science at
the University of Oxford. Before that Dr. Wood was an assistant professor of
Statistics at Columbia University and a research scientist at the Columbia
Center for Computational Learning Systems. He formerly was a postdoctoral
fellow of the Gatsby Computational Neuroscience Unit of the University College
London under Dr. Yee Whye Teh. He received his PhD from Brown University in
computer science under the supervision of Dr. Michael Black and Dr. Tom
Griffiths. Prior to his academic career he was a successful entrepreneur having
run and sold the content-based image retrieval company ToFish! to Time Warner
and serving as CEO of Interfolio. He started his career working at both the
Cornell Theory Center and subsequently the Lawrence Berkeley National
Laboratory. Dr. Wood holds 6 patents, has authored over 40 papers, received the
AISTATS best paper award in 2009, and has been awarded faculty research awards
from Xerox, Google and Amazon.